{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Convolutional Networks\n",
    "So far we have worked with deep fully-connected networks, using them to explore different optimization strategies and network architectures. Fully-connected networks are a good testbed for experimentation because they are very computationally efficient, but in practice all state-of-the-art results use convolutional networks instead.\n",
    "\n",
    "First you will implement several layer types that are used in convolutional networks. You will then use these layers to train a convolutional network on the CIFAR-10 dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# As usual, a bit of setup\n",
    "from __future__ import print_function\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from cs231n.classifiers.cnn import *\n",
    "from cs231n.data_utils import get_CIFAR10_data\n",
    "from cs231n.gradient_check import eval_numerical_gradient_array, eval_numerical_gradient\n",
    "from cs231n.layers import *\n",
    "from cs231n.fast_layers import *\n",
    "from cs231n.solver import Solver\n",
    "\n",
    "%matplotlib inline\n",
    "plt.rcParams['figure.figsize'] = (10.0, 8.0) # set default size of plots\n",
    "plt.rcParams['image.interpolation'] = 'nearest'\n",
    "plt.rcParams['image.cmap'] = 'gray'\n",
    "\n",
    "# for auto-reloading external modules\n",
    "# see http://stackoverflow.com/questions/1907993/autoreload-of-modules-in-ipython\n",
    "%load_ext autoreload\n",
    "%autoreload 2\n",
    "\n",
    "def rel_error(x, y):\n",
    "  \"\"\" returns relative error \"\"\"\n",
    "  return np.max(np.abs(x - y) / (np.maximum(1e-8, np.abs(x) + np.abs(y))))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X_train:  (49000, 3, 32, 32)\n",
      "y_train:  (49000,)\n",
      "X_val:  (1000, 3, 32, 32)\n",
      "y_val:  (1000,)\n",
      "X_test:  (1000, 3, 32, 32)\n",
      "y_test:  (1000,)\n"
     ]
    }
   ],
   "source": [
    "# Load the (preprocessed) CIFAR10 data.\n",
    "\n",
    "data = get_CIFAR10_data()\n",
    "for k, v in data.items():\n",
    "  print('%s: ' % k, v.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Convolution: Naive forward pass\n",
    "The core of a convolutional network is the convolution operation. In the file `cs231n/layers.py`, implement the forward pass for the convolution layer in the function `conv_forward_naive`. \n",
    "\n",
    "You don't have to worry too much about efficiency at this point; just write the code in whatever way you find most clear.\n",
    "\n",
    "You can test your implementation by running the following:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Testing conv_forward_naive\n",
      "difference:  2.21214764175e-08\n"
     ]
    }
   ],
   "source": [
    "x_shape = (2, 3, 4, 4)\n",
    "w_shape = (3, 3, 4, 4)\n",
    "x = np.linspace(-0.1, 0.5, num=np.prod(x_shape)).reshape(x_shape)\n",
    "w = np.linspace(-0.2, 0.3, num=np.prod(w_shape)).reshape(w_shape)\n",
    "b = np.linspace(-0.1, 0.2, num=3)\n",
    "\n",
    "conv_param = {'stride': 2, 'pad': 1}\n",
    "out, _ = conv_forward_naive(x, w, b, conv_param)\n",
    "correct_out = np.array([[[[-0.08759809, -0.10987781],\n",
    "                           [-0.18387192, -0.2109216 ]],\n",
    "                          [[ 0.21027089,  0.21661097],\n",
    "                           [ 0.22847626,  0.23004637]],\n",
    "                          [[ 0.50813986,  0.54309974],\n",
    "                           [ 0.64082444,  0.67101435]]],\n",
    "                         [[[-0.98053589, -1.03143541],\n",
    "                           [-1.19128892, -1.24695841]],\n",
    "                          [[ 0.69108355,  0.66880383],\n",
    "                           [ 0.59480972,  0.56776003]],\n",
    "                          [[ 2.36270298,  2.36904306],\n",
    "                           [ 2.38090835,  2.38247847]]]])\n",
    "\n",
    "# Compare your output to ours; difference should be around 2e-8\n",
    "print('Testing conv_forward_naive')\n",
    "print('difference: ', rel_error(out, correct_out))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Aside: Image processing via convolutions\n",
    "\n",
    "As fun way to both check your implementation and gain a better understanding of the type of operation that convolutional layers can perform, we will set up an input containing two images and manually set up filters that perform common image processing operations (grayscale conversion and edge detection). The convolution forward pass will apply these operations to each of the input images. We can then visualize the results as a sanity check."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/root/anaconda3/lib/python3.6/site-packages/ipykernel_launcher.py:3: DeprecationWarning: `imread` is deprecated!\n",
      "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n",
      "Use ``imageio.imread`` instead.\n",
      "  This is separate from the ipykernel package so we can avoid doing imports until\n",
      "/root/anaconda3/lib/python3.6/site-packages/ipykernel_launcher.py:10: DeprecationWarning: `imresize` is deprecated!\n",
      "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n",
      "Use ``skimage.transform.resize`` instead.\n",
      "  # Remove the CWD from sys.path while we load stuff.\n",
      "/root/anaconda3/lib/python3.6/site-packages/ipykernel_launcher.py:11: DeprecationWarning: `imresize` is deprecated!\n",
      "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n",
      "Use ``skimage.transform.resize`` instead.\n",
      "  # This is added back by InteractiveShellApp.init_path()\n"
     ]
    },
    {
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5EABsZ2YBCgSNIQJ7H8AXEYwleHIBkBEh6HYGUVTEFJSq94Suc2DjYawF4GGNhUEHYg84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\nM2t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0sKR1XbNer4cM9DqthX42eR69dlerFQ8fPuRrX/saX/nKV2jblk8++WTIhSnzW0Ck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\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EPczNTuN02hgdO0AsFOTa5UmC4QgTExPMzc1hteji/UQ8TiAQoCcWUQGgDzzwAF0NvvKN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\ncCsHpFy3YojlsdKCl2Lf4/EwNjZGKBR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j3mLGGBkBKMbMyAwY07lF1yLp86K9cLlcOkAH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\nMWLEiCfG/wcnrZJpR8BYMwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f3629856f98>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from scipy.misc import imread, imresize\n",
    "\n",
    "kitten, puppy = imread('kitten.jpg'), imread('puppy.jpg')\n",
    "# kitten is wide, and puppy is already square\n",
    "d = kitten.shape[1] - kitten.shape[0]\n",
    "kitten_cropped = kitten[:, d//2:-d//2, :]\n",
    "\n",
    "img_size = 200   # Make this smaller if it runs too slow\n",
    "x = np.zeros((2, 3, img_size, img_size))\n",
    "x[0, :, :, :] = imresize(puppy, (img_size, img_size)).transpose((2, 0, 1))\n",
    "x[1, :, :, :] = imresize(kitten_cropped, (img_size, img_size)).transpose((2, 0, 1))\n",
    "\n",
    "# Set up a convolutional weights holding 2 filters, each 3x3\n",
    "w = np.zeros((2, 3, 3, 3))\n",
    "\n",
    "# The first filter converts the image to grayscale.\n",
    "# Set up the red, green, and blue channels of the filter.\n",
    "w[0, 0, :, :] = [[0, 0, 0], [0, 0.3, 0], [0, 0, 0]]\n",
    "w[0, 1, :, :] = [[0, 0, 0], [0, 0.6, 0], [0, 0, 0]]\n",
    "w[0, 2, :, :] = [[0, 0, 0], [0, 0.1, 0], [0, 0, 0]]\n",
    "\n",
    "# Second filter detects horizontal edges in the blue channel.\n",
    "w[1, 2, :, :] = [[1, 2, 1], [0, 0, 0], [-1, -2, -1]]\n",
    "\n",
    "# Vector of biases. We don't need any bias for the grayscale\n",
    "# filter, but for the edge detection filter we want to add 128\n",
    "# to each output so that nothing is negative.\n",
    "b = np.array([0, 128])\n",
    "\n",
    "# Compute the result of convolving each input in x with each filter in w,\n",
    "# offsetting by b, and storing the results in out.\n",
    "out, _ = conv_forward_naive(x, w, b, {'stride': 1, 'pad': 1})\n",
    "\n",
    "def imshow_noax(img, normalize=True):\n",
    "    \"\"\" Tiny helper to show images as uint8 and remove axis labels \"\"\"\n",
    "    if normalize:\n",
    "        img_max, img_min = np.max(img), np.min(img)\n",
    "        img = 255.0 * (img - img_min) / (img_max - img_min)\n",
    "    plt.imshow(img.astype('uint8'))\n",
    "    plt.gca().axis('off')\n",
    "\n",
    "# Show the original images and the results of the conv operation\n",
    "plt.subplot(2, 3, 1)\n",
    "imshow_noax(puppy, normalize=False)\n",
    "plt.title('Original image')\n",
    "plt.subplot(2, 3, 2)\n",
    "imshow_noax(out[0, 0])\n",
    "plt.title('Grayscale')\n",
    "plt.subplot(2, 3, 3)\n",
    "imshow_noax(out[0, 1])\n",
    "plt.title('Edges')\n",
    "plt.subplot(2, 3, 4)\n",
    "imshow_noax(kitten_cropped, normalize=False)\n",
    "plt.subplot(2, 3, 5)\n",
    "imshow_noax(out[1, 0])\n",
    "plt.subplot(2, 3, 6)\n",
    "imshow_noax(out[1, 1])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Convolution: Naive backward pass\n",
    "Implement the backward pass for the convolution operation in the function `conv_backward_naive` in the file `cs231n/layers.py`. Again, you don't need to worry too much about computational efficiency.\n",
    "\n",
    "When you are done, run the following to check your backward pass with a numeric gradient check."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Testing conv_backward_naive function\n",
      "dx error:  1.15980316116e-08\n",
      "dw error:  2.24712647485e-10\n",
      "db error:  3.3726400665e-11\n"
     ]
    }
   ],
   "source": [
    "np.random.seed(231)\n",
    "x = np.random.randn(4, 3, 5, 5)\n",
    "w = np.random.randn(2, 3, 3, 3)\n",
    "b = np.random.randn(2,)\n",
    "dout = np.random.randn(4, 2, 5, 5)\n",
    "conv_param = {'stride': 1, 'pad': 1}\n",
    "\n",
    "dx_num = eval_numerical_gradient_array(lambda x: conv_forward_naive(x, w, b, conv_param)[0], x, dout)\n",
    "dw_num = eval_numerical_gradient_array(lambda w: conv_forward_naive(x, w, b, conv_param)[0], w, dout)\n",
    "db_num = eval_numerical_gradient_array(lambda b: conv_forward_naive(x, w, b, conv_param)[0], b, dout)\n",
    "\n",
    "out, cache = conv_forward_naive(x, w, b, conv_param)\n",
    "dx, dw, db = conv_backward_naive(dout, cache)\n",
    "\n",
    "# Your errors should be around 1e-8'\n",
    "print('Testing conv_backward_naive function')\n",
    "print('dx error: ', rel_error(dx, dx_num))\n",
    "print('dw error: ', rel_error(dw, dw_num))\n",
    "print('db error: ', rel_error(db, db_num))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Max pooling: Naive forward\n",
    "Implement the forward pass for the max-pooling operation in the function `max_pool_forward_naive` in the file `cs231n/layers.py`. Again, don't worry too much about computational efficiency.\n",
    "\n",
    "Check your implementation by running the following:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Testing max_pool_forward_naive function:\n",
      "difference:  4.16666651573e-08\n"
     ]
    }
   ],
   "source": [
    "x_shape = (2, 3, 4, 4)\n",
    "x = np.linspace(-0.3, 0.4, num=np.prod(x_shape)).reshape(x_shape)\n",
    "pool_param = {'pool_width': 2, 'pool_height': 2, 'stride': 2}\n",
    "\n",
    "out, _ = max_pool_forward_naive(x, pool_param)\n",
    "\n",
    "correct_out = np.array([[[[-0.26315789, -0.24842105],\n",
    "                          [-0.20421053, -0.18947368]],\n",
    "                         [[-0.14526316, -0.13052632],\n",
    "                          [-0.08631579, -0.07157895]],\n",
    "                         [[-0.02736842, -0.01263158],\n",
    "                          [ 0.03157895,  0.04631579]]],\n",
    "                        [[[ 0.09052632,  0.10526316],\n",
    "                          [ 0.14947368,  0.16421053]],\n",
    "                         [[ 0.20842105,  0.22315789],\n",
    "                          [ 0.26736842,  0.28210526]],\n",
    "                         [[ 0.32631579,  0.34105263],\n",
    "                          [ 0.38526316,  0.4       ]]]])\n",
    "\n",
    "# Compare your output with ours. Difference should be around 1e-8.\n",
    "print('Testing max_pool_forward_naive function:')\n",
    "print('difference: ', rel_error(out, correct_out))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Max pooling: Naive backward\n",
    "Implement the backward pass for the max-pooling operation in the function `max_pool_backward_naive` in the file `cs231n/layers.py`. You don't need to worry about computational efficiency.\n",
    "\n",
    "Check your implementation with numeric gradient checking by running the following:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Testing max_pool_backward_naive function:\n",
      "dx error:  3.27562514223e-12\n"
     ]
    }
   ],
   "source": [
    "np.random.seed(231)\n",
    "x = np.random.randn(3, 2, 8, 8)\n",
    "dout = np.random.randn(3, 2, 4, 4)\n",
    "pool_param = {'pool_height': 2, 'pool_width': 2, 'stride': 2}\n",
    "\n",
    "dx_num = eval_numerical_gradient_array(lambda x: max_pool_forward_naive(x, pool_param)[0], x, dout)\n",
    "\n",
    "out, cache = max_pool_forward_naive(x, pool_param)\n",
    "dx = max_pool_backward_naive(dout, cache)\n",
    "\n",
    "# Your error should be around 1e-12\n",
    "print('Testing max_pool_backward_naive function:')\n",
    "print('dx error: ', rel_error(dx, dx_num))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Fast layers\n",
    "Making convolution and pooling layers fast can be challenging. To spare you the pain, we've provided fast implementations of the forward and backward passes for convolution and pooling layers in the file `cs231n/fast_layers.py`.\n",
    "\n",
    "The fast convolution implementation depends on a Cython extension; to compile it you need to run the following from the `cs231n` directory:\n",
    "\n",
    "```bash\n",
    "python setup.py build_ext --inplace\n",
    "```\n",
    "\n",
    "The API for the fast versions of the convolution and pooling layers is exactly the same as the naive versions that you implemented above: the forward pass receives data, weights, and parameters and produces outputs and a cache object; the backward pass recieves upstream derivatives and the cache object and produces gradients with respect to the data and weights.\n",
    "\n",
    "**NOTE:** The fast implementation for pooling will only perform optimally if the pooling regions are non-overlapping and tile the input. If these conditions are not met then the fast pooling implementation will not be much faster than the naive implementation.\n",
    "\n",
    "You can compare the performance of the naive and fast versions of these layers by running the following:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Testing conv_forward_fast:\n",
      "Naive: 3.270658s\n",
      "Fast: 0.007895s\n",
      "Speedup: 414.282397x\n",
      "Difference:  4.92640785149e-11\n",
      "\n",
      "Testing conv_backward_fast:\n",
      "Naive: 6.023093s\n",
      "Fast: 0.007603s\n",
      "Speedup: 792.231717x\n",
      "dx difference:  1.94976477535e-11\n",
      "dw difference:  3.681156828e-13\n",
      "db difference:  0.0\n"
     ]
    }
   ],
   "source": [
    "from cs231n.fast_layers import conv_forward_fast, conv_backward_fast\n",
    "from time import time\n",
    "np.random.seed(231)\n",
    "x = np.random.randn(100, 3, 31, 31)\n",
    "w = np.random.randn(25, 3, 3, 3)\n",
    "b = np.random.randn(25,)\n",
    "dout = np.random.randn(100, 25, 16, 16)\n",
    "conv_param = {'stride': 2, 'pad': 1}\n",
    "\n",
    "t0 = time()\n",
    "out_naive, cache_naive = conv_forward_naive(x, w, b, conv_param)\n",
    "t1 = time()\n",
    "out_fast, cache_fast = conv_forward_fast(x, w, b, conv_param)\n",
    "t2 = time()\n",
    "\n",
    "print('Testing conv_forward_fast:')\n",
    "print('Naive: %fs' % (t1 - t0))\n",
    "print('Fast: %fs' % (t2 - t1))\n",
    "print('Speedup: %fx' % ((t1 - t0) / (t2 - t1)))\n",
    "print('Difference: ', rel_error(out_naive, out_fast))\n",
    "\n",
    "t0 = time()\n",
    "dx_naive, dw_naive, db_naive = conv_backward_naive(dout, cache_naive)\n",
    "t1 = time()\n",
    "dx_fast, dw_fast, db_fast = conv_backward_fast(dout, cache_fast)\n",
    "t2 = time()\n",
    "\n",
    "print('\\nTesting conv_backward_fast:')\n",
    "print('Naive: %fs' % (t1 - t0))\n",
    "print('Fast: %fs' % (t2 - t1))\n",
    "print('Speedup: %fx' % ((t1 - t0) / (t2 - t1)))\n",
    "print('dx difference: ', rel_error(dx_naive, dx_fast))\n",
    "print('dw difference: ', rel_error(dw_naive, dw_fast))\n",
    "print('db difference: ', rel_error(db_naive, db_fast))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Testing pool_forward_fast:\n",
      "Naive: 0.266918s\n",
      "fast: 0.001571s\n",
      "speedup: 169.858140x\n",
      "difference:  0.0\n",
      "\n",
      "Testing pool_backward_fast:\n",
      "Naive: 0.812749s\n",
      "speedup: 92.477836x\n",
      "dx difference:  0.0\n"
     ]
    }
   ],
   "source": [
    "from cs231n.fast_layers import max_pool_forward_fast, max_pool_backward_fast\n",
    "np.random.seed(231)\n",
    "x = np.random.randn(100, 3, 32, 32)\n",
    "dout = np.random.randn(100, 3, 16, 16)\n",
    "pool_param = {'pool_height': 2, 'pool_width': 2, 'stride': 2}\n",
    "\n",
    "t0 = time()\n",
    "out_naive, cache_naive = max_pool_forward_naive(x, pool_param)\n",
    "t1 = time()\n",
    "out_fast, cache_fast = max_pool_forward_fast(x, pool_param)\n",
    "t2 = time()\n",
    "\n",
    "print('Testing pool_forward_fast:')\n",
    "print('Naive: %fs' % (t1 - t0))\n",
    "print('fast: %fs' % (t2 - t1))\n",
    "print('speedup: %fx' % ((t1 - t0) / (t2 - t1)))\n",
    "print('difference: ', rel_error(out_naive, out_fast))\n",
    "\n",
    "t0 = time()\n",
    "dx_naive = max_pool_backward_naive(dout, cache_naive)\n",
    "t1 = time()\n",
    "dx_fast = max_pool_backward_fast(dout, cache_fast)\n",
    "t2 = time()\n",
    "\n",
    "print('\\nTesting pool_backward_fast:')\n",
    "print('Naive: %fs' % (t1 - t0))\n",
    "print('speedup: %fx' % ((t1 - t0) / (t2 - t1)))\n",
    "print('dx difference: ', rel_error(dx_naive, dx_fast))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Convolutional \"sandwich\" layers\n",
    "Previously we introduced the concept of \"sandwich\" layers that combine multiple operations into commonly used patterns. In the file `cs231n/layer_utils.py` you will find sandwich layers that implement a few commonly used patterns for convolutional networks."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(2, 3, 8, 8)\n",
      "Testing conv_relu_pool\n",
      "dx error:  9.59113262192e-09\n",
      "dw error:  5.80239113733e-09\n",
      "db error:  1.01463434118e-09\n"
     ]
    }
   ],
   "source": [
    "from cs231n.layer_utils import conv_relu_pool_forward, conv_relu_pool_backward\n",
    "np.random.seed(231)\n",
    "x = np.random.randn(2, 3, 16, 16)\n",
    "w = np.random.randn(3, 3, 3, 3)\n",
    "b = np.random.randn(3,)\n",
    "dout = np.random.randn(2, 3, 8, 8)\n",
    "conv_param = {'stride': 1, 'pad': 1}\n",
    "pool_param = {'pool_height': 2, 'pool_width': 2, 'stride': 2}\n",
    "\n",
    "out, cache = conv_relu_pool_forward(x, w, b, conv_param, pool_param)\n",
    "\n",
    "dx, dw, db = conv_relu_pool_backward(dout, cache)\n",
    "\n",
    "dx_num = eval_numerical_gradient_array(lambda x: conv_relu_pool_forward(x, w, b, conv_param, pool_param)[0], x, dout)\n",
    "dw_num = eval_numerical_gradient_array(lambda w: conv_relu_pool_forward(x, w, b, conv_param, pool_param)[0], w, dout)\n",
    "db_num = eval_numerical_gradient_array(lambda b: conv_relu_pool_forward(x, w, b, conv_param, pool_param)[0], b, dout)\n",
    "\n",
    "print('Testing conv_relu_pool')\n",
    "print('dx error: ', rel_error(dx_num, dx))\n",
    "print('dw error: ', rel_error(dw_num, dw))\n",
    "print('db error: ', rel_error(db_num, db))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Testing conv_relu:\n",
      "dx error:  1.52186199803e-09\n",
      "dw error:  2.7020226461e-10\n",
      "db error:  1.45127239359e-10\n"
     ]
    }
   ],
   "source": [
    "from cs231n.layer_utils import conv_relu_forward, conv_relu_backward\n",
    "np.random.seed(231)\n",
    "x = np.random.randn(2, 3, 8, 8)\n",
    "w = np.random.randn(3, 3, 3, 3)\n",
    "b = np.random.randn(3,)\n",
    "dout = np.random.randn(2, 3, 8, 8)\n",
    "conv_param = {'stride': 1, 'pad': 1}\n",
    "\n",
    "out, cache = conv_relu_forward(x, w, b, conv_param)\n",
    "dx, dw, db = conv_relu_backward(dout, cache)\n",
    "\n",
    "dx_num = eval_numerical_gradient_array(lambda x: conv_relu_forward(x, w, b, conv_param)[0], x, dout)\n",
    "dw_num = eval_numerical_gradient_array(lambda w: conv_relu_forward(x, w, b, conv_param)[0], w, dout)\n",
    "db_num = eval_numerical_gradient_array(lambda b: conv_relu_forward(x, w, b, conv_param)[0], b, dout)\n",
    "\n",
    "print('Testing conv_relu:')\n",
    "print('dx error: ', rel_error(dx_num, dx))\n",
    "print('dw error: ', rel_error(dw_num, dw))\n",
    "print('db error: ', rel_error(db_num, db))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Three-layer ConvNet\n",
    "Now that you have implemented all the necessary layers, we can put them together into a simple convolutional network.\n",
    "\n",
    "Open the file `cs231n/classifiers/cnn.py` and complete the implementation of the `ThreeLayerConvNet` class. Run the following cells to help you debug:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Sanity check loss\n",
    "After you build a new network, one of the first things you should do is sanity check the loss. When we use the softmax loss, we expect the loss for random weights (and no regularization) to be about `log(C)` for `C` classes. When we add regularization this should go up."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Initial loss (no regularization):  2.30258506359\n",
      "Initial loss (with regularization):  2.50860006214\n"
     ]
    }
   ],
   "source": [
    "model = ThreeLayerConvNet()\n",
    "\n",
    "N = 50\n",
    "X = np.random.randn(N, 3, 32, 32)\n",
    "y = np.random.randint(10, size=N)\n",
    "\n",
    "loss, grads = model.loss(X, y)\n",
    "print('Initial loss (no regularization): ', loss)\n",
    "\n",
    "model.reg = 0.5\n",
    "loss, grads = model.loss(X, y)\n",
    "print('Initial loss (with regularization): ', loss)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Gradient check\n",
    "After the loss looks reasonable, use numeric gradient checking to make sure that your backward pass is correct. When you use numeric gradient checking you should use a small amount of artifical data and a small number of neurons at each layer. Note: correct implementations may still have relative errors up to 1e-2."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "W1 max relative error: 1.380104e-04\n",
      "W2 max relative error: 1.822723e-02\n",
      "W3 max relative error: 3.064049e-04\n",
      "b1 max relative error: 3.477652e-05\n",
      "b2 max relative error: 2.516375e-03\n",
      "b3 max relative error: 7.945660e-10\n"
     ]
    }
   ],
   "source": [
    "num_inputs = 2\n",
    "input_dim = (3, 16, 16)\n",
    "reg = 0.0\n",
    "num_classes = 10\n",
    "np.random.seed(231)\n",
    "X = np.random.randn(num_inputs, *input_dim)\n",
    "y = np.random.randint(num_classes, size=num_inputs)\n",
    "\n",
    "model = ThreeLayerConvNet(num_filters=3, filter_size=3,\n",
    "                          input_dim=input_dim, hidden_dim=7,\n",
    "                          dtype=np.float64)\n",
    "loss, grads = model.loss(X, y)\n",
    "for param_name in sorted(grads):\n",
    "    f = lambda _: model.loss(X, y)[0]\n",
    "    param_grad_num = eval_numerical_gradient(f, model.params[param_name], verbose=False, h=1e-6)\n",
    "    e = rel_error(param_grad_num, grads[param_name])\n",
    "    print('%s max relative error: %e' % (param_name, rel_error(param_grad_num, grads[param_name])))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Overfit small data\n",
    "A nice trick is to train your model with just a few training samples. You should be able to overfit small datasets, which will result in very high training accuracy and comparatively low validation accuracy."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(Iteration 1 / 30) loss: 2.414060\n",
      "(Epoch 0 / 15) train acc: 0.190000; val_acc: 0.128000\n",
      "(Iteration 2 / 30) loss: 2.609504\n",
      "(Epoch 1 / 15) train acc: 0.230000; val_acc: 0.094000\n",
      "(Iteration 3 / 30) loss: 2.113380\n",
      "(Iteration 4 / 30) loss: 1.971811\n",
      "(Epoch 2 / 15) train acc: 0.310000; val_acc: 0.098000\n",
      "(Iteration 5 / 30) loss: 1.676728\n",
      "(Iteration 6 / 30) loss: 1.801782\n",
      "(Epoch 3 / 15) train acc: 0.570000; val_acc: 0.191000\n",
      "(Iteration 7 / 30) loss: 1.652683\n",
      "(Iteration 8 / 30) loss: 1.598651\n",
      "(Epoch 4 / 15) train acc: 0.570000; val_acc: 0.194000\n",
      "(Iteration 9 / 30) loss: 1.070849\n",
      "(Iteration 10 / 30) loss: 1.408982\n",
      "(Epoch 5 / 15) train acc: 0.740000; val_acc: 0.188000\n",
      "(Iteration 11 / 30) loss: 0.816042\n",
      "(Iteration 12 / 30) loss: 0.807953\n",
      "(Epoch 6 / 15) train acc: 0.820000; val_acc: 0.256000\n",
      "(Iteration 13 / 30) loss: 0.971160\n",
      "(Iteration 14 / 30) loss: 0.568949\n",
      "(Epoch 7 / 15) train acc: 0.860000; val_acc: 0.236000\n",
      "(Iteration 15 / 30) loss: 0.394380\n",
      "(Iteration 16 / 30) loss: 0.401405\n",
      "(Epoch 8 / 15) train acc: 0.910000; val_acc: 0.194000\n",
      "(Iteration 17 / 30) loss: 0.723469\n",
      "(Iteration 18 / 30) loss: 0.258560\n",
      "(Epoch 9 / 15) train acc: 0.910000; val_acc: 0.169000\n",
      "(Iteration 19 / 30) loss: 0.242372\n",
      "(Iteration 20 / 30) loss: 0.235556\n",
      "(Epoch 10 / 15) train acc: 0.940000; val_acc: 0.199000\n",
      "(Iteration 21 / 30) loss: 0.212628\n",
      "(Iteration 22 / 30) loss: 0.126721\n",
      "(Epoch 11 / 15) train acc: 0.940000; val_acc: 0.213000\n",
      "(Iteration 23 / 30) loss: 0.113127\n",
      "(Iteration 24 / 30) loss: 0.270010\n",
      "(Epoch 12 / 15) train acc: 0.970000; val_acc: 0.204000\n",
      "(Iteration 25 / 30) loss: 0.067624\n",
      "(Iteration 26 / 30) loss: 0.081088\n",
      "(Epoch 13 / 15) train acc: 1.000000; val_acc: 0.207000\n",
      "(Iteration 27 / 30) loss: 0.029606\n",
      "(Iteration 28 / 30) loss: 0.051089\n",
      "(Epoch 14 / 15) train acc: 1.000000; val_acc: 0.209000\n",
      "(Iteration 29 / 30) loss: 0.027549\n",
      "(Iteration 30 / 30) loss: 0.025578\n",
      "(Epoch 15 / 15) train acc: 1.000000; val_acc: 0.209000\n"
     ]
    }
   ],
   "source": [
    "np.random.seed(231)\n",
    "\n",
    "num_train = 100\n",
    "small_data = {\n",
    "  'X_train': data['X_train'][:num_train],\n",
    "  'y_train': data['y_train'][:num_train],\n",
    "  'X_val': data['X_val'],\n",
    "  'y_val': data['y_val'],\n",
    "}\n",
    "\n",
    "model = ThreeLayerConvNet(weight_scale=1e-2)\n",
    "\n",
    "solver = Solver(model, small_data,\n",
    "                num_epochs=15, batch_size=50,\n",
    "                update_rule='adam',\n",
    "                optim_config={\n",
    "                  'learning_rate': 1e-3,\n",
    "                },\n",
    "                verbose=True, print_every=1)\n",
    "solver.train()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plotting the loss, training accuracy, and validation accuracy should show clear overfitting:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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57jjzzDMPWifsm9/8Jj//+c8BWLlyJX/9618ZM2bMQfeZPn06M2fOBOD0009n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\n6WCdCmQppd+nlN4JvBRYDvw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EN8Dp74XpZ8OAFgsIF7tlav8e2Fafe/wtKw8ObM0fbbXS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\nTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f3625be6128>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.subplot(2, 1, 1)\n",
    "plt.plot(solver.loss_history, 'o')\n",
    "plt.xlabel('iteration')\n",
    "plt.ylabel('loss')\n",
    "\n",
    "plt.subplot(2, 1, 2)\n",
    "plt.plot(solver.train_acc_history, '-o')\n",
    "plt.plot(solver.val_acc_history, '-o')\n",
    "plt.legend(['train', 'val'], loc='upper left')\n",
    "plt.xlabel('epoch')\n",
    "plt.ylabel('accuracy')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Train the net\n",
    "By training the three-layer convolutional network for one epoch, you should achieve greater than 40% accuracy on the training set:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(Iteration 1 / 980) loss: 2.304740\n",
      "(Epoch 0 / 1) train acc: 0.103000; val_acc: 0.107000\n",
      "(Iteration 21 / 980) loss: 2.132736\n",
      "(Iteration 41 / 980) loss: 1.890223\n",
      "(Iteration 61 / 980) loss: 1.790478\n",
      "(Iteration 81 / 980) loss: 1.745026\n",
      "(Iteration 101 / 980) loss: 1.892161\n",
      "(Iteration 121 / 980) loss: 1.890149\n",
      "(Iteration 141 / 980) loss: 1.964029\n",
      "(Iteration 161 / 980) loss: 1.741861\n",
      "(Iteration 181 / 980) loss: 1.886438\n",
      "(Iteration 201 / 980) loss: 1.958140\n",
      "(Iteration 221 / 980) loss: 1.789366\n",
      "(Iteration 241 / 980) loss: 1.693459\n",
      "(Iteration 261 / 980) loss: 1.507454\n",
      "(Iteration 281 / 980) loss: 1.622408\n",
      "(Iteration 301 / 980) loss: 1.845511\n",
      "(Iteration 321 / 980) loss: 1.737180\n",
      "(Iteration 341 / 980) loss: 1.680309\n",
      "(Iteration 361 / 980) loss: 1.749498\n",
      "(Iteration 381 / 980) loss: 1.420701\n",
      "(Iteration 401 / 980) loss: 1.685015\n",
      "(Iteration 421 / 980) loss: 1.733135\n",
      "(Iteration 441 / 980) loss: 1.591582\n",
      "(Iteration 461 / 980) loss: 1.700545\n",
      "(Iteration 481 / 980) loss: 1.492935\n",
      "(Iteration 501 / 980) loss: 1.404087\n",
      "(Iteration 521 / 980) loss: 1.712702\n",
      "(Iteration 541 / 980) loss: 1.459696\n",
      "(Iteration 561 / 980) loss: 1.607686\n",
      "(Iteration 581 / 980) loss: 1.232315\n",
      "(Iteration 601 / 980) loss: 1.641778\n",
      "(Iteration 621 / 980) loss: 1.560701\n",
      "(Iteration 641 / 980) loss: 1.642898\n",
      "(Iteration 661 / 980) loss: 1.651685\n",
      "(Iteration 681 / 980) loss: 1.799905\n",
      "(Iteration 701 / 980) loss: 1.447530\n",
      "(Iteration 721 / 980) loss: 1.538806\n",
      "(Iteration 741 / 980) loss: 1.893407\n",
      "(Iteration 761 / 980) loss: 1.408911\n",
      "(Iteration 781 / 980) loss: 2.160169\n",
      "(Iteration 801 / 980) loss: 1.850402\n",
      "(Iteration 821 / 980) loss: 1.542580\n",
      "(Iteration 841 / 980) loss: 1.441510\n",
      "(Iteration 861 / 980) loss: 1.779246\n",
      "(Iteration 881 / 980) loss: 1.656049\n",
      "(Iteration 901 / 980) loss: 1.455208\n",
      "(Iteration 921 / 980) loss: 1.820103\n",
      "(Iteration 941 / 980) loss: 1.526195\n",
      "(Iteration 961 / 980) loss: 1.639982\n",
      "(Epoch 1 / 1) train acc: 0.484000; val_acc: 0.485000\n"
     ]
    }
   ],
   "source": [
    "model = ThreeLayerConvNet(weight_scale=0.001, hidden_dim=500, reg=0.001)\n",
    "\n",
    "solver = Solver(model, data,\n",
    "                num_epochs=1, batch_size=50,\n",
    "                update_rule='adam',\n",
    "                optim_config={\n",
    "                  'learning_rate': 1e-3,\n",
    "                },\n",
    "                verbose=True, print_every=20)\n",
    "solver.train()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Visualize Filters\n",
    "You can visualize the first-layer convolutional filters from the trained network by running the following:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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498/oiD0v157hcz/zyRzmOz7eVXfqQx/E/B7zu1a2YewdeI0x5kqc5/zWx/dj3qhMY364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\nYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f36274f00b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from cs231n.vis_utils import visualize_grid\n",
    "\n",
    "grid = visualize_grid(model.params['W1'].transpose(0, 2, 3, 1))\n",
    "plt.imshow(grid.astype('uint8'))\n",
    "plt.axis('off')\n",
    "plt.gcf().set_size_inches(5, 5)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Spatial Batch Normalization\n",
    "We already saw that batch normalization is a very useful technique for training deep fully-connected networks. Batch normalization can also be used for convolutional networks, but we need to tweak it a bit; the modification will be called \"spatial batch normalization.\"\n",
    "\n",
    "Normally batch-normalization accepts inputs of shape `(N, D)` and produces outputs of shape `(N, D)`, where we normalize across the minibatch dimension `N`. For data coming from convolutional layers, batch normalization needs to accept inputs of shape `(N, C, H, W)` and produce outputs of shape `(N, C, H, W)` where the `N` dimension gives the minibatch size and the `(H, W)` dimensions give the spatial size of the feature map.\n",
    "\n",
    "If the feature map was produced using convolutions, then we expect the statistics of each feature channel to be relatively consistent both between different imagesand different locations within the same image. Therefore spatial batch normalization computes a mean and variance for each of the `C` feature channels by computing statistics over both the minibatch dimension `N` and the spatial dimensions `H` and `W`."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Spatial batch normalization: forward\n",
    "\n",
    "In the file `cs231n/layers.py`, implement the forward pass for spatial batch normalization in the function `spatial_batchnorm_forward`. Check your implementation by running the following:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Before spatial batch normalization:\n",
      "  Shape:  (2, 3, 4, 5)\n",
      "  Means:  [ 9.33463814  8.90909116  9.11056338]\n",
      "  Stds:  [ 3.61447857  3.19347686  3.5168142 ]\n",
      "After spatial batch normalization:\n",
      "  Shape:  (2, 3, 4, 5)\n",
      "  Means:  [  6.18949336e-16   5.99520433e-16  -1.22124533e-16]\n",
      "  Stds:  [ 0.99999962  0.99999951  0.9999996 ]\n",
      "After spatial batch normalization (nontrivial gamma, beta):\n",
      "  Shape:  (2, 3, 4, 5)\n",
      "  Means:  [ 6.  7.  8.]\n",
      "  Stds:  [ 2.99999885  3.99999804  4.99999798]\n"
     ]
    }
   ],
   "source": [
    "np.random.seed(231)\n",
    "# Check the training-time forward pass by checking means and variances\n",
    "# of features both before and after spatial batch normalization\n",
    "\n",
    "N, C, H, W = 2, 3, 4, 5\n",
    "x = 4 * np.random.randn(N, C, H, W) + 10\n",
    "\n",
    "print('Before spatial batch normalization:')\n",
    "print('  Shape: ', x.shape)\n",
    "print('  Means: ', x.mean(axis=(0, 2, 3)))\n",
    "print('  Stds: ', x.std(axis=(0, 2, 3)))\n",
    "\n",
    "# Means should be close to zero and stds close to one\n",
    "gamma, beta = np.ones(C), np.zeros(C)\n",
    "bn_param = {'mode': 'train'}\n",
    "out, _ = spatial_batchnorm_forward(x, gamma, beta, bn_param)\n",
    "print('After spatial batch normalization:')\n",
    "print('  Shape: ', out.shape)\n",
    "print('  Means: ', out.mean(axis=(0, 2, 3)))\n",
    "print('  Stds: ', out.std(axis=(0, 2, 3)))\n",
    "\n",
    "# Means should be close to beta and stds close to gamma\n",
    "gamma, beta = np.asarray([3, 4, 5]), np.asarray([6, 7, 8])\n",
    "out, _ = spatial_batchnorm_forward(x, gamma, beta, bn_param)\n",
    "print('After spatial batch normalization (nontrivial gamma, beta):')\n",
    "print('  Shape: ', out.shape)\n",
    "print('  Means: ', out.mean(axis=(0, 2, 3)))\n",
    "print('  Stds: ', out.std(axis=(0, 2, 3)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "After spatial batch normalization (test-time):\n",
      "  means:  [-0.08034406  0.07562881  0.05716371  0.04378383]\n",
      "  stds:  [ 0.96718744  1.0299714   1.02887624  1.00585577]\n"
     ]
    }
   ],
   "source": [
    "np.random.seed(231)\n",
    "# Check the test-time forward pass by running the training-time\n",
    "# forward pass many times to warm up the running averages, and then\n",
    "# checking the means and variances of activations after a test-time\n",
    "# forward pass.\n",
    "N, C, H, W = 10, 4, 11, 12\n",
    "\n",
    "bn_param = {'mode': 'train'}\n",
    "gamma = np.ones(C)\n",
    "beta = np.zeros(C)\n",
    "for t in range(50):\n",
    "  x = 2.3 * np.random.randn(N, C, H, W) + 13\n",
    "  spatial_batchnorm_forward(x, gamma, beta, bn_param)\n",
    "bn_param['mode'] = 'test'\n",
    "x = 2.3 * np.random.randn(N, C, H, W) + 13\n",
    "a_norm, _ = spatial_batchnorm_forward(x, gamma, beta, bn_param)\n",
    "\n",
    "# Means should be close to zero and stds close to one, but will be\n",
    "# noisier than training-time forward passes.\n",
    "print('After spatial batch normalization (test-time):')\n",
    "print('  means: ', a_norm.mean(axis=(0, 2, 3)))\n",
    "print('  stds: ', a_norm.std(axis=(0, 2, 3)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Spatial batch normalization: backward\n",
    "In the file `cs231n/layers.py`, implement the backward pass for spatial batch normalization in the function `spatial_batchnorm_backward`. Run the following to check your implementation using a numeric gradient check:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dx error:  2.78664819776e-07\n",
      "dgamma error:  7.09748171136e-12\n",
      "dbeta error:  3.27560872528e-12\n"
     ]
    }
   ],
   "source": [
    "np.random.seed(231)\n",
    "N, C, H, W = 2, 3, 4, 5\n",
    "x = 5 * np.random.randn(N, C, H, W) + 12\n",
    "gamma = np.random.randn(C)\n",
    "beta = np.random.randn(C)\n",
    "dout = np.random.randn(N, C, H, W)\n",
    "\n",
    "bn_param = {'mode': 'train'}\n",
    "fx = lambda x: spatial_batchnorm_forward(x, gamma, beta, bn_param)[0]\n",
    "fg = lambda a: spatial_batchnorm_forward(x, gamma, beta, bn_param)[0]\n",
    "fb = lambda b: spatial_batchnorm_forward(x, gamma, beta, bn_param)[0]\n",
    "\n",
    "dx_num = eval_numerical_gradient_array(fx, x, dout)\n",
    "da_num = eval_numerical_gradient_array(fg, gamma, dout)\n",
    "db_num = eval_numerical_gradient_array(fb, beta, dout)\n",
    "\n",
    "_, cache = spatial_batchnorm_forward(x, gamma, beta, bn_param)\n",
    "dx, dgamma, dbeta = spatial_batchnorm_backward(dout, cache)\n",
    "print('dx error: ', rel_error(dx_num, dx))\n",
    "print('dgamma error: ', rel_error(da_num, dgamma))\n",
    "print('dbeta error: ', rel_error(db_num, dbeta))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Extra Credit Description\n",
    "If you implement any additional features for extra credit, clearly describe them here with pointers to any code in this or other files if applicable."
   ]
  }
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